Infrared Local Positioning Systems (IRLPS) offer a cost-effective and accurate alternative for indoor localization, where GNSS signals are typically not available. Despite their potential, IRLPS face significant challenges such as noise, multipath effects, multiple access interference, and calibration requirements, which limit their performance. In response, this work explores the integration of machine learning by proposing a Feedforward Neural Network (FNN) trained on energy measurements collected from a quadrant photodiode. We conducted a thorough analysis of hyperparameters and an ablation study across five neural network topologies and identified configurations that balance accuracy and model complexity. Experimental evaluations in a controlled indoor environment (2.4×2.4×3.4 m3) demonstrate that even simple FNN architectures can generalize well and achieve a high accuracy, with 90% of the positioning errors being below 0.05 m.

Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisition. The concept of channel charting reduces this effort by modeling and projecting the manifold of channel state information (CSI) onto a 2D coordinate space. So far, researchers have only applied this concept to active radio localization, where a mobile device intentionally and actively emits a specific signal.In this paper, we apply channel charting to passive localization. We use a pedestrian dead reckoning (PDR) system to estimate a target's velocity and derive a distance matrix from it. We then use this matrix to learn a distance-preserving embedding in 2D space, which serves as a fingerprinting model. In our experiments, we deploy six nodes in a fully connected ultra-wideband (UWB) mesh network to show that our method achieves high localization accuracy, with an average error of just 0.24 m, even when we train and test on different targets.

The rapid growth of 5G and future 6G networks requires efficient and scalable radio access network (RAN) densification, especially in dense urban and industrial areas. Traditional planning uses manual surveys and simple propagation models, but these lack spatial accuracy and adaptability. Stochastic RF simulation tools often fail to model real-world conditions, such as material properties and geometry. This leads to poor site selection, higher costs, and rollout delays.This paper proposes an AI-based framework that combines high-resolution 3D modeling, Digital Twin technology, and deterministic ray tracing. It uses aerial and ground imagery to build detailed 3D models, enhanced with object detection and material classification through segmentation models. These models enable automatic feature extraction for RF simulation and planning. The system uses open-source 3D tools, vision transformers for segmentation, and a simulation engine with antenna radiation patterns and material-aware propagation. Tests in urban and campus settings show better prediction accuracy, less manual work, and lower costs than traditional methods. Results show that AI and Digital Twins improve and automate network deployment.

Federated learning (FL) enables multiple devices to collaboratively train a global model while maintaining data on local servers. Each device trains the model on its local server and shares only the model updates (i.e., gradient weights) during the aggregation step. A significant challenge in FL is managing the feature distribution of novel and unbalanced data across devices. In this paper, we propose an FL approach using few-shot learning and aggregation of the model weights on a global server. We introduce a dynamic early stopping method to balance out-of-distribution classes based on representation learning, specifically utilizing the maximum mean discrepancy of feature embeddings between local and global models. An exemplary application of FL is to orchestrate machine learning models along highways for interference classification based on snapshots from global navigation satellite system (GNSS) receivers. Extensive experiments on four GNSS datasets from two real-world highways and controlled environments demonstrate that our FL method surpasses state-of-the-art techniques in adapting to both novel interference classes and multipath scenarios. https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/federated_learning

Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems. Despite their adaptability and efficiency, LLMs have not been extensively explored for signal processing tasks, particularly in the domain of global navigation satellite system (GNSS) interference monitoring. GNSS interference monitoring is essential to ensure the reliability of vehicle localization on roads, a critical requirement for numerous applications. However, GNSS-based positioning is vulnerable to interference from jamming devices, which can compromise its accuracy. The primary objective is to identify, classify, and mitigate these interferences. Interpreting GNSS snapshots and the associated interferences presents significant challenges due to the inherent complexity, including multipath effects, diverse interference types, varying sensor characteristics, and satellite constellations. In this paper, we extract features from a large GNSS dataset and employ LLaVA to retrieve relevant information from an extensive knowledge base. We employ prompt engineering to interpret the interferences and environmental factors, and utilize t-SNE to analyze the feature embeddings. Our findings demonstrate that the proposed method is capable of visual and logical reasoning within the GNSS context. Furthermore, our pipeline outperforms state-of-the-art machine learning models in interference classification tasks. Github: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss

Localization based on channel impulse responses (CIRs) of radio frequency (RF) signals yields centimeter-accurate positions under optimal line-of-sight (LOS) propagation conditions. However, in real indoor environments, e.g., in car manufacturing, non-line-of-sight (NLOS) situations dominate. Here, multipath propagation affects the time-of-arrival (ToA) estimation and downstream multilateration and localization accuracy. The detection and subsequent mitigation of NLOS per transceiver line compensates for these effects. To detect NLOS, the state-of-the-art employs either supervised or unsupervised learning methods that require the acquisition of expensive reference data or do not generalize to changes or unknown environments. This is due to, among other things, the fact that they cannot exploit spatial and temporal information from CIR signal streams.Thus, we propose a generative deep state space model (SSM) for NLOS detection on CIRs that exploits time and space. Our ultra-wideband (UWB) experiments show that our dynamical variational autoencoder (DVAE) detects NLOS signals from sequences of CIRs more accurately than the state-of-the-art and is robust to unknown environments.

Artificial Intelligence (AI)-based radio fingerprinting (FP) outperforms classic localization methods in propagation environments with strong multipath effects. However, the model and data orchestration of FP are time-consuming and costly, as it requires many reference positions and extensive measurement campaigns for each environment. Instead, modern unsupervised and self-supervised learning schemes require less reference data for localization, but either their accuracy is low or they require additional sensor information, rendering them impractical.In this paper we propose a self-supervised learning framework that pre-trains a general transformer (TF) neural network on 5G channel measurements that we collect on-the-fly without expensive equipment. Our novel pretext task randomly masks and drops input information to learn to reconstruct it. So, it implicitly learns the spatiotemporal patterns and information of the propagation environment that enable FP-based localization. Most interestingly, when we optimize this pre-trained model for localization in a given environment, it achieves the accuracy of state-of-the-art methods but requires ten times less reference data and significantly reduces the time from training to operation.

Modern radio frequency based positioning systems exploit multipath propagation to achieve accurate and robust positioning at a minimum effort in infrastructure. A key concept is exploitation of multipath component (MPC) delays from channel measurements, which have a direct relation to the geometry of the environment. This is a challenging task given complex multipath-rich environments and limited bandwidths. However, downstream tasks suffer from false or missed detections, which is why reliable MPC detection and delay estimation is crucial. We propose an MPC delay estimation pipeline based on a Transformer (TF) neural network, which implicitly estimates the number and delays of the MPCs. We achieve subsample accuracy without using computational expensive super-resolution techniques. Our approach outperforms state-of-the art on detection and delay estimation of MPCs on different bandwidths. We also show that our approach can easily be fine-tuned on real world data with very few labeled data samples, making it a well-suited candidate for real world deployments.

Indoor radio environments often consist of areas with mixed propagation conditions. In LoS-dominated areas, classic ToF methods reliably return optimal (accurate) positions, while in NLoS-dominated areas (AI-based) fingerprinting methods are required. However, these fingerprinting methods are only cost-efficient if they are used exclusively in NLoS-dominated areas due to an expensive life cycle management. Systems that are both accurate and cost-efficient in LoS- and NLoS-dominated areas require an identification of those areas to select the optimal localization method. In this paper we propose methods for uncertainty estimation of AI-based fingerprinting to determine its validity. Our experiments show that we can implicitly switch between classic and fingerprinting-based approaches to reliably estimate accurate positions, even in NLoS-dominated radio environments. Our approach works even if the AI models are only trained on radio data in certain areas of the environment. In contrast to the state-of-the-art, our approach intrinsically identifies the spatial boundaries of the AI model, and thus does not require prior area identification.

Fingerprint-based indoor positioning has attracted a lot of interest due to its potential to meet a positional accuracy that enables many location-based 5G indoor services. However, the accuracy of fingerprinting decreases with changes in the environment which prevents positioning in new scenarios. On the other hand, naively acquiring up-to-date training data from the changed environment to retrain the model is often time-consuming. It is unclear whether after a change in the environment, a fingerprint model can be (data-)efficiently updated.This paper examines the generalizability (with respect to accuracy, robustness, and effort in recording data) of state-of-the-art fingerprint models based on a convolutional neural network (CNN) in realistic setups with changes in the environment. We propose a transfer learning (TL) method that exploits realistic synthetic Channel State Information (CSI) obtained with the Quasi Deterministic Radio channel Generator (QuaDRiGa), used to pre-train the CNN-based fingerprint model so that it can be adapted to any real (NLoS) propagation scenario with a low number of real training samples. Our experiments show that the positioning accuracy using fine-tuning improves by 37% in changed and by 19% in new environments.

Channel measurements at sufficiently high bandwidth in multipath-rich environments include a variety of delay information, which, if accurately extracted, can be exploited for accurate positioning. While previous methods are limited in practice as they rely on iteratively extracting a fixed number of delays, we instead formulate the delay extraction problem as a time series segmentation task. For this, we propose a pipeline built upon the U-Net convolutional neural network architecture. Unlike the state of the art our pipeline extracts an arbitrary number of delays without prior knowledge, includes a threshold for weighting between detection rate and false alarms, and does not rely on computationally demanding operations such as eigenvalue decomposition. We evaluate the presented method with synthetic data of different noise configurations and signal bandwidths and a publicly available dataset, achieving considerable performance gains w.r.t. detection performance and tracking accuracy. Furthermore, we show that the proposed method is far less computationally demanding in inference.

Ultra-wideband (UWB) systems based on Channel State Information (CSI) estimate the position of mobile nodes within an environment by using Channel Impulse Responses (CIRs) of multiple stationary nodes. These contain spatial information caused by environment interactions such as reflections and scattering. To estimate positions from CSI of stationary nodes, we must transmit them to a centralized node. This introduces considerable communication overhead.We present a large-scale study to determine whether CSI can be compressed into a small set of underlying latent variables that describe the most valuable information. We evaluate multiple neural network architectures containing encoding (compressing) and decoding (reconstructing) components and compare them to the state-of-the-art compression techniques Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). We show that fully connected autoencoders achieve the lowest error, outperforming both DCT and DWT. Further experiments prove that the reconstructed CSI can be used for positioning with only mild performance deterioration at a compression of >97% and even when trained on a different environment.

Many radio-based positioning systems use time-of-arrival (ToA). We obtain it from the first and direct path of arrival (FDPoA) in a corresponding set of multipath components (MPC) of the underlying channel state information (CSI). While detection of the FDPoA under Line-of-Sight (LoS) is simple, it is prone to errors in environments with specular and diffuse reflections, as well as nonlinear diffraction, absorption, and transmission of a signal. Such Obstructed- or Non-Line-of-Sight (OLoS, NLoS) situations lead to incorrect FDPoA and consequently to incorrect ToA estimates and inaccurate positions. State-of-the-art estimators are computationally expensive and usually fail with O/NLoS at low signal-to-noise ratios (SNRs).We propose a deep learning (DL) approach to identify optimal FDPoAs as ToA directly from the raw CSI. Our 1D Convolutional Neural Network (CNN) learns the spatial distribution of MPCs of the CSI to predict correct estimates of the ToA. To train our DL model, we use QuaDRiGa to generate datasets with CIRs and ground truth ToAs for realistic 5G channel models. We found that Delay Spread (DS), k-Factor (kF), and SNR are appropriate metrics to cover most LoS-NLoS constellations in realistic datasets. We compare our DL model with state-of-the-art estimators such as threshold (PEAK), inflection point (IFP), and MUSIC and show that we consistently outperform them by about 17% for SNRs below -10 dB.

Digital Contact Tracing (CT) protocols based on Bluetooth are best implemented at the system level to save resources and preserve security aspects. Combined with a government-monitored software platform, these CT-protocols can then be used to support controlling pandemics such as COVID-19. However, it is unclear how these protocols have to be parameterized to ensure the most accurate and reliable CT.This paper describes how we derived optimal parameters for a decentralized CT from extensive measurement campaigns that we carried out together with Deutsche Telekom (DT) and SAP under the supervision of the Robert Koch Institut (RKI). We examined the Google/Apple Exposure Notification Framework (ENF), which in combination with the front-end, i.e., the German Corona-Warn-App (CWA), enables digital CT in Germany. With centimeter accurate optical reference systems we show that optimal parameters are application-specific. However, they cause impractical high resource costs. In contrast, optimized general parameters offer an everyday compromise between energy costs, applicability, accuracy, and reliability of the ENF.

The main challenge in estimating human velocity from noisy Inertial Measurement Units (IMUs) are the errors that accumulate by integrating noisy accelerometer signals over a long time. Known approaches that work on step length estimation are optimized for a specific application, sensor position, and movement type, require an exhaustive (manual) parameter tuning, and can thus not be applied to other movement types or to a broader range of applications. Moreover, varying dynamics (as they are present for instance in sports applications) cause abrupt and unpredictable changes in step frequency or step length and hence result in erroneous velocity estimates. We use machine learning (ML) and deep learning (DL) to estimate a human's velocity. Our approach is robust to varying motion states and orientation changes in dynamic situations. On data from a single un-calibrated IMU, our novel recurrent model not only outperforms the state-of-the-art on instantaneous velocity (≤0.10 m/s) and on traveled distance (≤29 m/km). It can also generalize to different and varying rates of motion and provides accurate and precise velocity estimates.

We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.

With free movement and multi-user capabilities, there is demand to open up Virtual Reality (VR) for large spaces. However, the cost of accurate camera-based tracking grows with the size of the space and the number of users. No-pose (NP) tracking is cheaper, but so far it cannot accurately and stably estimate the yaw orientation of the user's head in the long-run. Our novel yaw orientation estimation combines a single inertial sensor located at the human's head with inaccurate positional tracking. We exploit that humans tend to walk in their viewing direction and that they also tolerate some orientation drift. We classify head and body motion and estimate heading drift to enable low-cost long-time stable head orientation in NP tracking on 100 m×100 m. Our evaluation shows that we estimate heading reasonably well.

Kalman filters (KFs) are popular methods to estimate position information from a set of time-of-flight (ToF) values in radio frequency (RF)-based locating systems. Such filters are proven to be optimal under zero-mean Gaussian error distributions. In presence of multipath propagation ToF measurement errors drift due to small-scale motion. This results in changing phases of the multipath components (MPCs) which cause a drift on the ToF measurements. Thus, on a short-term scale the ToF measurements have a non-constant bias that changes while moving. KFs cannot distinguish between the drifting measurement errors and the true motion of the tracked object. Hence, very rigid motion models have to be used for the KF which commonly causes the filters to diverge. Therefore, the KF cannot resolve the short-term errors of consecutive measurements and the long-term motion of the tracked object. This paper presents a data-driven approach that uses training sequences to derive a near-optimal position estimator. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) learns to interpret drifting errors in ToF measurements of a tracked dynamic object directly from raw ToF data. Our evaluation shows that our approach outperforms state-of-the-art KFs on both synthetically generated and real-world dynamic motion trajectories that include drifting ToF measurement errors.

No-Pose (NP) tracking systems rely on a single sensor located at the user's head to determine the position of the head. They estimate the head orientation with inertial sensors and analyze the body motion to compensate their drift. However with orientation drift, VR users implicitly lean their heads and bodies sidewards. Hence, to determine the sensor drift and to explicitly adjust the orientation of the VR display there is a need to understand and consider both the user's head and body orientations. This paper studies the effects of head orientation drift around the yaw axis on the user's absolute head and body orientations when walking naturally in the VR. We study how much drift accumulates over time, how a user experiences and tolerates it, and how a user applies strategies to compensate for larger drifts.

Pose tracking does not yet reliably work in large-scale interactive multi-user VR. Our novel head orientation estimation combines a single inertial sensor located at the user's head with inaccurate positional tracking. We exploit that users tend to walk in their viewing direction and classify head and body motion to estimate heading drift. This enables low-cost long-time stable head orientation. We evaluate our method and show that we sustain immersion.

This paper presents a novel approach for the augmentation of social behaviors in virtual reality (VR). We designed three visual transformations for behavioral phenomena crucial to everyday social interactions: eye contact, joint attention, and grouping. To evaluate the approach, we let users interact socially in a virtual museum using a large-scale multi-user tracking environment. Using a between-subject design (N = 125) we formed groups of five participants. Participants were represented as simplified avatars and experienced the virtual museum simultaneously, either with or without the augmentations. Our results indicate that our approach can significantly increase social presence in multi-user environments and that the augmented experience appears more thought-provoking. Furthermore, the augmentations seem also to affect the actual behavior of participants with regard to more eye contact and more focus on avatars/objects in the scene. We interpret these findings as first indicators for the potential of social augmentations to impact social perception and behavior in VR.

Redirected Walking (RDW) manipulates a scene that is displayed to VR users so that they unknowingly compensate for scene motion and can thus explore a large virtual world on a limited space. So far, mostly visual manipulation techniques have been studied.